use previous checkpoint

Author: stevhliu

docs/source/en/using-diffusers/text-img2vid.md

@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+ <!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # Video generation
 
-Video generation models add a temporal dimension to bring images, or frames, together to create a video. These models are trained on large-scale datasets of high-quality text-video pairs to learn how to combine the modalities to ensure the generated video is coherent and realistic.
+Video generation models include a temporal dimension to bring images, or frames, together to create a video. These models are trained on large-scale datasets of high-quality text-video pairs to learn how to combine the modalities to ensure the generated video is coherent and realistic.
 
 [Explore](https://huggingface.co/models?other=video-generation) some of the more popular open-source video generation models available from Diffusers below.
 
@@ -23,7 +23,7 @@ Video generation models add a temporal dimension to bring images, or frames, tog
 
 The CogVideoX family also includes models capable of generating videos from images and videos in addition to text. The image-to-video models are indicated by **I2V** in the checkpoint name, and they should be used with the [`CogVideoXImageToVideoPipeline`]. The regular checkpoints support video-to-video through the [`CogVideoXVideoToVideoPipeline`].
 
-The example below demonstrates how to generate a video from an image and text prompt with [THUDM/CogVideoX1.5-5B-I2V](https://huggingface.co/THUDM/CogVideoX1.5-5B-I2V).
+The example below demonstrates how to generate a video from an image and text prompt with [THUDM/CogVideoX-5b-I2V](https://huggingface.co/THUDM/CogVideoX-5b-I2V).
 
 ```py
 import torch
@@ -33,7 +33,7 @@ from diffusers.utils import export_to_video, load_image
 prompt = "A vast, shimmering ocean flows gracefully under a twilight sky, its waves undulating in a mesmerizing dance of blues and greens. The surface glints with the last rays of the setting sun, casting golden highlights that ripple across the water. Seagulls soar above, their cries blending with the gentle roar of the waves. The horizon stretches infinitely, where the ocean meets the sky in a seamless blend of hues. Close-ups reveal the intricate patterns of the waves, capturing the fluidity and dynamic beauty of the sea in motion."
 image = load_image(image="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cogvideox/cogvideox_rocket.png")
 pipe = CogVideoXImageToVideoPipeline.from_pretrained(
-    "THUDM/CogVideoX1.5-5B-I2V",
+    "THUDM/CogVideoX-5b-I2V",
     torch_dtype=torch.bfloat16
 )